mouse ko sgrna pooled library  (Addgene inc)

Journal: Cell metabolism

Article Title: Functional Genomics In Vivo Reveal Metabolic Dependencies of Pancreatic Cancer Cells.

doi: 10.1016/j.cmet.2020.10.017

Figure Lengend Snippet: Figure 1. Metabolism-Focused CRISPR Screens In Vivo Reveal Metabolic Dependencies of Pancreatic Tumors (A) Schematic of genetics screens to identify metabolic dependencies of KP pancreatic cancer specifically in vivo. (B) Cumulative frequency curve of represented guides in genetic screens. (C) Gene scores of in vivo versus in vitro genetic screens of KP pancreatic cancer growth. (D) Volcano plot of differential gene scores comparing in vivo against in vitro conditions (left). Top 20 genes scoring as differentially required in vivo. Genes involved in specific metabolic pathways are indicated (right). (E) Gene sets enriched in differentially required genes in vivo versus in vitro for pancreatic cancer growth. The heatmap generated by iPAGE represents the extent to which each gene set is enriched among the genes that are essential for tumor growth in vivo. See also Figure S1.

Article Snippet: CRISPR Cas9 sgRNA KO mouse metabolism library generated in this study has been deposited to Addgene (ID 160129).

Techniques: CRISPR, In Vivo, In Vitro, Generated

Journal: Cell metabolism

Article Title: Functional Genomics In Vivo Reveal Metabolic Dependencies of Pancreatic Cancer Cells.

doi: 10.1016/j.cmet.2020.10.017

Figure Lengend Snippet: Figure 3. Heme Synthesis Is a Metabolic Dependency of Kras-Driven Tumors In Vivo (A) Immunoblot of HMBS in the indicated KP pancreas and KP lung cancer cell lines. GAPDH was used as loading control. (B) Fold change in cell number (log2) of the indicated KP pancreas and KP lung cancer cell lines after culturing in vitro for the indicated durations (mean ± SD, n = 3). ***p < 0.001 versus sgControl. (C) Tumor weights of the indicated KP pancreas and KP lung tumors engrafted subcutaneously in C57BL/6J mice (box and whisker, n = 8). *p < 0.05, ***p < 0.001 versus sgControl (left). Images of the indicated KP pancreas and KP lung tumors (right). (D) Immunoblot of HMOX1 in KP pancreas and KP lung cancer cells grown in vitro under normoxia, under hypoxia (0.5% oxygen) for 48 h, and in subcutaneous tumors. GAPDH was used as loading control. (E) Immunoblots of HMOX1 and HMBS in the indicated KP pancreas cell lines. GAPDH was used as loading control. (F) Relative tumor weights of the indicated KP pancreas Hmbs_KO tumors engrafted subcutaneously in C57BL/6J mice (box and whisker, n = 23). *p < 0.05 versus control (top). Representative image of the indicated KP pancreas Hmbs_KO tumors (bottom). (G) Schematic of competition assay using PDAC patient-derived xenograft cells infected with the indicated sgRNAs. Cells were then engrafted subcutaneously in NSG mice (left). Relative fold change in sgRNA abundance (log2) from the PDX (mean ± SD, n = 5). **p < 0.01 versus sgControl (right). (H) Disease-free survival rates of TCGA PDAC patients with high or low heme synthesis gene expressions. Weighted average expressions of CPOX, HMBS, PPOX, and UROS were used (low heme n = 83, high heme n = 28). See also Figure S2.

Article Snippet: CRISPR Cas9 sgRNA KO mouse metabolism library generated in this study has been deposited to Addgene (ID 160129).

Techniques: In Vivo, Western Blot, Control, In Vitro, Whisker Assay, Competitive Binding Assay, Derivative Assay, Infection

Journal: Cell

Article Title: Genome-wide CRISPR Screens in T Helper Cells Reveal Pervasive Crosstalk between Activation and Differentiation

doi: 10.1016/j.cell.2018.11.044

Figure Lengend Snippet: Overview of the Experimental KO Screening Strategy (A) In our culture system, naive, ex vivo T cells are differentiated into Th2 cells by IL4. Potential alternative T cell fates that may be open to genetically perturbed cells are indicated. In vivo , T cells develop into different subtypes dependent on stimuli. (B) The retrovirus is based on murine stem cell virus (MSCV), encoding one sgRNA per virus, and allows for BFP and puro selection. For the screening we have used a pool of plasmids, encoding over 86,000 sgRNAs, from all of which we produced viruses. The library is subcloned from a previous mouse sgRNA library ( Tzelepis et al., 2016 ). (C) For genome-wide screens, we pool cells from up to 30 mice. After infection and puromycin selection, the cells are sorted based on fluorescence for the investigated gene. sgRNAs affecting gene expression are identified by genomic PCR. Differential sgRNA expression analysis then allows us to find genes affecting either viability (drop-out screen) or differentiation. (D) The top enriched and depleted genes (“hits”) were analyzed based on their dynamics measured by RNA-seq, ATAC-seq, and ChIP-seq. (E) Particularly interesting genes were further validated by individual KO and RNA-seq. (F) By using all this data and curating the literature, we provide a Th2 gene regulatory network.

Article Snippet: To produce the pooled library pMSCV-U6gRNA(lib)-PGKpuroT2ABFP (Addgene: #104861) the sgRNA part of a previous mouse KO sgRNA pooled library ( ) (Addgene: #67988) was PCR-amplified using the primers gib_sgRNAlib_fwd/rev.

Techniques: Ex Vivo, In Vivo, Virus, Selection, Produced, Genome Wide, Infection, Fluorescence, Gene Expression, Expressing, RNA Sequencing, ChIP-sequencing